Create app.py

app.py

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+from transformers import pipeline, GPT2Tokenizer, GPT2LMHeadModel
+import torch
+import streamlit as st
+
+# Load GPT-2 model and tokenizer
+model_name = "openai-community/gpt2-xl"
+model = GPT2LMHeadModel.from_pretrained(model_name)
+tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+
+# Set up sentiment analysis pipeline
+sentiment_analysis = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
+
+# Streamlit UI
+st.title("GPT-2 Movie Review Sentiment Analysis")
+
+# Input text for sentiment analysis
+input_text = st.text_area("Enter movie review:", "")
+
+# Choose analysis type
+analysis_type = st.radio("Select analysis type:", ["Zero-shot", "One-shot", "Few-shot"])
+
+if st.button("Analyze Sentiment"):
+    # Perform sentiment analysis based on the selected type
+    if analysis_type == "Zero-shot":
+        results = sentiment_analysis(input_text)
+    elif analysis_type == "One-shot":
+        prompt = "This movie review is about a "
+        input_text = prompt + input_text
+        results = sentiment_analysis(input_text)
+    elif analysis_type == "Few-shot":
+        prompt = "This movie review is about a positive topic. " + input_text
+        results = sentiment_analysis(prompt)
+
+    # Display results
+    st.write("Sentiment:", results[0]['label'])
+    st.write("Confidence:", results[0]['score'])
+
+# Note: This is a basic example, and you might need to fine-tune it based on your specific use case and requirements.